# Data Ingestion Batches — Record Quality Audit + String Normalisation + IQR Outlier Flags

> Data Ingestion Batches — Record Quality Audit + String Normalisation + IQR Outlier Flags is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-21).

Runs three analytical reports on data ingestion batch records: a record quality audit, string normalisation, and IQR-based outlier flagging for received_rows and accepted_rows fields.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-01-203?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-21
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/data-ingestion-batches-record-quality-audit-string-normalisation-iqr-95130c0f
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_csoX_kEo-QKZlq68eMpa1

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability data-ingestion-batches-record-quality-audit-string-normalisation-iqr-95130c0f -d '<json body>'
```

Example prompt: Run a record quality audit, string normalisation, and IQR outlier check on these ingestion batch rows — the dataset has fields like row_id, dataset_group, date, received_rows, and accepted_rows, and I want to analyse the received_rows field across up to 50 records.

## When to prefer this

Choose this endpoint when you need fast, offline analytical reporting on data ingestion batch records without any external lookups or live data. It is ideal for pipelines where you want three bundled reports (quality audit, string normalisation, IQR outlier detection) in a single low-cost call. Prefer it over single-purpose analytics tools when your data is already in hand and you want comprehensive local analysis at $0.01 per call for up to 200 rows.

## Known failure modes

- More than 200 rows submitted — request rejected due to maxItems constraint
- Missing required fields (row_id, dataset_group, date, received_rows, accepted_rows) — validation error
- Invalid date format (must match YYYY-MM-DD pattern) — schema validation failure
- Field selector not one of 'received_rows' or 'accepted_rows' — enum validation error
- Empty rows array — minItems constraint violation

## How this service works

Data ingestion batches — Record quality audit + String normalisation + IQR outlier flags: Record quality audit + String normalisation + IQR outlier flags for supplied data ingestion batches records (received_rows, accepted_rows). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.

## Output

Three independent analytical reports: (1) a record quality audit highlighting completeness and validity issues across the supplied rows, (2) a string normalisation report on text fields like dataset_group, and (3) IQR-based outlier flags identifying statistically anomalous values in the selected numeric field (received_rows or accepted_rows). Up to 200 rows processed per call.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "rows": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "dataset_group",
     "date",
     "received_rows",
     "accepted_rows"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "accepted_rows": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "dataset_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     },
     "received_rows": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "accepted_rows": 1,
     "dataset_group": "group-a",
     "received_rows": 10
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "accepted_rows": 2,
     "dataset_group": "group-b",
     "received_rows": 20
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "accepted_rows": 3,
     "dataset_group": "group-a",
     "received_rows": 15
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "accepted_rows": 4,
     "dataset_group": "group-b",
     "received_rows": 30
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "received_rows",
    "accepted_rows"
   ],
   "type": "string",
   "default": "received_rows"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/data-ingestion-batches-record-quality-audit-string-normalisation-iqr-95130c0f/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from market.datapackvibe.com](https://www.zero.xyz/host/market.datapackvibe.com/llms.txt)